A great deal of attention has been given to automated machine-learning techniques. However, there are a number of situations in which conventional machine-learning techniques are inadequate. One such situation occurs when items are to be classified into categories and the content of the items and/or the category definitions vary significantly over time. This problem often is referred to as “concept drift”. An example is where one wishes to employ an automated process for the purpose of classifying news articles (perhaps thousands of new articles each day) into various topic areas based on the text content of the articles.
While a number of approaches have been taken to address this problem, additional improvements in performance would be beneficial.